Automatic Construction of Machine Translation Knowledge Using Translation Literalness

When machine translation (MT) knowledge is automatically constructed from bilingual corpora, redundant rules are acquired due to translation variety. These rules increase ambiguity or cause incorrect MT results. To overcome this problem, we constrain the sentences used for knowledge extraction to "the appropriate bilingual sentences for the MT." In this paper, we propose a method using translation literalness to select appropriate sentences or phrases. The translation correspondence rate (TCR) is defined as the literalness measure. Based on the TCR, two automatic construction methods are tested. One is to filter the corpus before rule acquisition. The other is to split the acquisition process into two phases, where a bilingual sentence is divided into literal parts and the other parts before different generalizations are applied. The effects are evaluated by the MT quality, and about 4.9% of MT results were improved by the latter method.